k-Nearest Neighbors Classifier

Classifier based on the k-nearest neighbors algorithm

Documentation

Classifier based on the k-nearest neighbors algorithm

Configuration:

• n_neighbors

  Number of neighbors to use by default for kneighbors() queries.

• weights

  weight function used in prediction. Possible values:

  • ‘uniform’ : uniform weights. All points in each neighborhood are weighted equally.

  • ‘distance’ : weight points by the inverse of their distance. in this case, closer neighbors of a query point will have a greater influence than neighbors which are further away.

  • [callable] : a user-defined function which accepts an array of distances, and returns an array of the same shape containing the weights.

• algorithm

  Algorithm used to compute the nearest neighbors:

  • ‘ball_tree’ will use BallTree

  • ‘kd_tree’ will use KDTree

  • ‘brute’ will use a brute-force search.

  • ‘auto’ will attempt to decide the most appropriate algorithm based on the values passed to fit() method.

  Note: fitting on sparse input will override the setting of this parameter, using brute force.

• leaf_size

  Leaf size passed to BallTree or KDTree. This can affect the speed of the construction and query, as well as the memory required to store the tree. The optimal value depends on the nature of the problem.

• metric

  the distance metric to use for the tree. The default metric is minkowski, and with p=2 is equivalent to the standard Euclidean metric. See the documentation of the DistanceMetric class for a list of available metrics. If metric is “precomputed”, X is assumed to be a distance matrix and must be square during fit. X may be a sparse graph, in which case only “nonzero” elements may be considered neighbors.

• p

  Power parameter for the Minkowski metric. When p = 1, this is equivalent to using manhattan_distance (l1), and euclidean_distance (l2) for p = 2. For arbitrary p, minkowski_distance (l_p) is used.

• n_jobs

  The number of parallel jobs to run for neighbors search. None means 1 unless in a joblib.parallel_backend context. -1 means using all processors. See n_jobs for more details. Doesn’t affect fit() method.

Attributes:

Input ports:

Output ports:

out-modelmodel

Output model

Definition

Input ports

Output ports

out-model

model

Output model

class node_knn.KNeighborsClassifier